Solution overview. To develop the sequential recommender, we adopt the popular Transformer architecture [1]} as the backbone of our approach. It is built on the self-attention mechanism, taking as input item embeddings and positional embeddings at each time step. Unlike previous related studies [2]}, we don't include any additional components (e.g., adaptors) into the Transformer architecture, but instead learn transferable item representations for feeding the backbone.
The key novelty of our approach lies in the new item representation scheme for sequential recommenders. In this scheme, we first map item text into a vector of discrete indices (called item code), and then employ these indices to lookup the code embedding table for deriving item representations. Such a scheme can be denoted as “text \(\Longrightarrow \)  code \(\Longrightarrow \)  representation”, which removes the tight binding between item text and item representations.
In order to learn and transfer such item representations, we further propose specific strategies for contrastive recommender pre-training and cross-domain recommender fine-tuning.
